Learning Scores Research Articles

Geogebra Software as A Guideline for Meaningful Learning of Mathematical Content for Ninth- Grade Students in Ecuador – A Secondary Publication

The introduction of the GeoGebra software led to a considerable increase in the interactivity of the didactic sequence of learning mathematical contents, a scenario that highlighted the inclusion of the meaningful learning model as the basis of pedagogical proposals. In this sense, the objective of this article was to determine the relationship of the GeoGebra software in the learning of mathematics in ninth-grade students in the General Education (GE) of the Yaruquí Public Educational Unit. Relational research with a non-experimental and transversal design was carried out to measure the variables in 44 ninth-grade students through the collection of data using valid and reliable instruments. The results indicated that a large number of students deemed the interface and interactivity of the GeoGebra software as “appropriate” (> 60%) and showed high levels of information acquisition and incorporation of new knowledge (68.2%). The correlational analysis revealed positive directions and moderate levels between the dimensions of the two variables (0.430 < Rho < 0.515); overall, a direct and moderate correlation was observed between GeoGebra software and mathematical learning (Rho = 0.549, R2 = 0.301). Finally, it was concluded that higher scores in the dimensions of GeoGebra software would manifest in high scores for meaningful learning of mathematical content.

A Study on College Students’ Learning Attitude During Synchronous and Asynchronous Video-Based Group Activities for English Presentation — A Secondary Publication

The purpose of the study is to explore college students’ perceptions/attitudes during synchronous and asynchronous group activities. The survey comprised a 10-item, 5-point Likert-type questionnaire and open-ended questions. In the study, two groups, a synchronous group and an asynchronous group, participated in four activities during one semester. For the data collection, the researcher surveyed 116 students who are in their 1st year of college and collected their final tests, an oral presentation. The findings are as follows. Firstly, between the two groups, the synchronous group showed higher mean scores for online group participation. Secondly, the synchronous group also indicated higher mean scores for English learning, speaking confidence, and interest in learning English. Lastly, both groups mentioned communication with team members, improvement in English speaking and pronunciation, increased self-confidence, and other elements as advantages of group work. In contrast, disadvantages included technology problems, decreased self-confidence, inconveniences when meeting with team members online, and others. The implications for the study are: firstly, online group activity can reduce isolation and increase interaction; secondly, the online video-based platform, Flipgrid, can help students improve English speaking skills by observing their own or peers’ videos; lastly, instructors can selectively use the synchronous and asynchronous group activities to suit their educational needs.

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study.

Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

The effectiveness of using situational awareness and case-based seminars in a comprehensive nursing skill practice course for undergraduate nursing students: a quasi-experimental study

BackgroundNurses play an important role in healthcare development. The increasing demands for nurses mean that nursing schools at the undergraduate level have the responsibility to ensure patient safety and quality care through a well-designed curriculum. This research aimed to evaluate the effect of the teaching method combined with situational awareness and case-based seminars in a comprehensive nursing skills practice course on the level of self-directed learning, professional identity, academic self-efficacy, theoretical scores, practical scores, teaching satisfaction, and student competence among nursing students.MethodsThe research population comprised was of the grades of 2019 and 2020 at Wannan Medical College in Anhui Province, China (n = 169, response rate 77.88%). The observation group from grade 2020 used the teaching method combined with situational awareness and case-based seminars, whereas the control group from grade 2019 used the traditional teaching mode. General information, self-directed learning, a professional identity, and academic self-efficacy were compared between the two groups. This research used means and standard deviations, chi-square, the Shapiro–Wilk test, and an independent sample t-test for statistical analyses.ResultsCompared with the control group, the total scores for self-directed learning, professional identity, and academic self-efficacy were higher in the observation group (78.80 ± 7.89 vs 60.21 ± 7.44, 63.39 ± 7.87 vs 52.35 ± 7.68, and 22.31 ± 3.30 vs 21.28 ± 2.31, respectively, with P < 0.05 for all scores). More significant improvements were made in the observation group on the level of theoretical scores (81.39 ± 3.32 vs 76.28 ± 5.90) and practical scores (93.32 ± 4.70 vs 90.67 ± 5.09) (P < 0.05). Meanwhile, teaching satisfaction, which includes teaching method (66/18 vs 32/53) and teacher-student interaction (72/12 vs 34/51), and student competence, which includes team cooperation (67/17 vs 39/46), critical thinking (60/24 vs 31/54), and communication skills (67/17 vs 38/47) after the intervention (P < 0.05). There was no significant difference in social persuasion (P > 0.05).ConclusionThe teaching method combined with situational awareness and case-based seminars in a comprehensive nursing skills practice course has the potential to improve the level of self-directed learning, professional identity, and academic self-efficacy, and it increases theoretical scores, practical scores, teaching satisfaction, and student competence.

Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning.

Forecast verification is a crucial task for assessing the predictive power of prognostic model forecasts and it is usually implemented by checking quality-based skill scores. In this article, we propose a novel approach to realize forecast verification focusing not just on the forecast quality but rather on its value. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive nonoccurring events, and that, on the other hand, a miss of an isolated event has a worse impact than a miss of a single event, which is part of several consecutive occurrences. Relying on this idea, we introduce a notion of value-weighted skill scores giving greater importance to the value of the prediction rather than to its quality. Then, we introduce an ensemble strategy to maximize quality-based and value-weighted skill scores independently of one another. We test it on the predictions provided by deep learning methods for binary classification in the case of four applications concerned with pollution, space weather, stock price, and IoT data stream forecasting. Our experimental studies show that using the ensemble strategy for maximizing the value-weighted skill scores generally improves both the value and quality of the forecast.

Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease - The Gut and Obesity in Asia (GO-ASIA) Study.

The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non-alcoholic fatty liver disease (NAFLD/MASLD). We evaluated the performance of machine learning (ML) and non-patented scores for ruling out SF among NAFLD/MASLD patients. Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria). Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p < 0.001, each). ML models showed 7%-12% better discrimination than FIB-4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB-4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set). ML with clinical, anthropometric data and simple blood investigations perform better than FIB-4 for ruling out SF in biopsy-proven Asian NAFLD/MASLD patients.

Learning Semantic Alignment Using Global Features and Multi-Scale Confidence

Semantic alignment aims to establish pixel correspondences between images based on semantic consistency. It can serve as a fundamental component for various downstream computer vision tasks, such as style transfer and exemplar-based colorization, etc. Many existing methods use local features and their cosine similarities to infer semantic alignment. However, they struggle with significant intra-class variation of objects, such as appearance, size, etc. In other words, contents with the same semantics tend to be significantly different in vision. To address this issue, we propose a novel deep neural network of which the core lies in global feature enhancement and adaptive multi-scale inference. Specifically, two modules are proposed: an enhancement transformer for enhancing semantic features with global awareness; a probabilistic correlation module for adaptively fusing multi-scale information based on the learned confidence scores. We use the unified network architecture to achieve two types of semantic alignment, namely, cross-object semantic alignment and cross-domain semantic alignment. Experimental results demonstrate that our method achieves competitive performance on five standard cross-object semantic alignment benchmarks, and outperforms the state of the arts in cross-domain semantic alignment.

Research on the Development of Process-oriented Mathematics Learning Diagnosis and Scoring Criteria - Focusing on the ‘Quadratic Equations’ in Middle School -

Research on the Development of Process-oriented Mathematics Learning Diagnosis and Scoring Criteria - Focusing on the ‘Quadratic Equations’ in Middle School -

PENERAPAN MODEL PEMBELAJARAN JIGSAW UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS IV TEMA SELALU BERHEMAT ENERGI BERDASARKAN TAKSONOMI BLOOM

This research applies classroom action research (PTK) where the action process in implementing learning carries out two cycle stages, namely cycles (scale I and II). Each time it contains the process of preparation, implementation, observation and planning. The research subjects were 20 class IV students at MI Darus Sholihin for the 2022-2023 academic year. Furthermore, the aim of this research is that researchers want to find out whether the application of the jigsaw type learning model can provide a significant increase in changes in students' learning outcomes, teaching materials that always save energy and their use, science subjects. Then, to show curiosity about the results of students' learning achievements, a process of evaluating student learning outcomes based on Bloom's taxonomy is needed. From this review, it is clear that the implementation of the jigsaw type teaching model has experienced an improvement in each cycle, as evidenced by the previous cycle (period I) the students' learning scores only obtained an average of 64.95 with a percentage of 53% and in the cycle (period II) the average score was 80. 1 with a percentage of 94.73%, resulting in an increase in the average score of 15.15 (41.73) students who had completed and met the classification standards (criteria) determined by the researcher.

Strategic Insights: Understanding the Interplay of Adaptability and Consistency in Multinational and National Organizations

This research delves into the intricate dynamics of organizational cultures, specifically focusing on the dimensions of adaptability and consistency. Through a comprehensive study involving 2 multinational and 2 national companies, the research analyzes key components such as creative change, customer focus, core values, agreement, and coordination and integration. The findings reveal that multinational companies exhibit significantly higher adaptability, driven particularly by strong scores in creative change and organizational learning. Although no significant disparities were identified in overall consistency scores, multinational organizations consistently demonstrated higher scores across core values, agreement, and coordination and integration. The study emphasizes the interdependence of adaptability and consistency traits, providing actionable insights for leaders aiming to foster resilient and effective organizational cultures. While the research has certain limitations, such as a reliance on self-reported data and a cross-sectional design, it lays a foundation for future explorations into the nuanced dimensions of organizational dynamics across diverse global contexts.

Development of Autonomous Learning Model to Enhance Inflight Safety-Based Competence for Cabin Crew

Currently, the aviation business is a popular service that helps people travel conveniently, quickly, and safely. People in the airline business have become more stressed with the need to learn about inflight safety-based competence to effectively serve the broad range of passengers&amp;rsquo; needs. One reason for this is the nature of their jobs, which also affects their learning. Therefore, this research study developed an autonomous learning model to enhance inflight safety-based competence for cabin crew and then studied the effects of using an autonomous learning model to enhance inflight safety-based competence for cabin crew. This study was conducted in two parts using research and development methodology (R&amp;amp;D). The first phase identified problems and needs in learning about inflight safety-based competence, while the second stage examined the results of using the developed learning model. The results revealed that an autonomous learning model consisted of the model&amp;rsquo;s purpose, identification and management of learning goals, knowledge development of learning strategies, the trainer&amp;rsquo;s role, practical ideas, and reflection on and evaluation of the learner&amp;rsquo;s learning. Furthermore, the cabin crew members had improved scores in inflight safety-based competence learning in four areas: safety policy, risk management, safety promotion, and safety assurance, compared before participating in the autonomous learning model testing. The vital learning promotion course for cabin crew dealing with the inflight safety of the passengers. The developed autonomous learning model should enhance the inflight safety-based competence of cabin crew.

Contrastive learning-based histopathological features infer molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images

The artificial intelligence-powered computational pathology has led to significant improvements in the speed and precision of tumor diagnosis, while also exhibiting substantial potential to infer genetic mutations and gene expression levels. However, current studies remain limited in predicting molecular subtypes and clinical outcomes in breast cancer. In this paper, we proposed a weakly supervised contrastive learning framework to address this challenge. Our framework first performed contrastive learning pretraining on a large number of unlabeled patches tiled from whole slide images (WSIs) to extract patch-level features. The gated attention mechanism was leveraged to aggregate patch-level features to produce slide feature that was then applied to various downstream tasks. To confirm the effectiveness of the proposed method, three public cohorts and one external independent cohort of breast cancer have been used to conducted evaluation experiments. The predictive powers of our model to infer gene expression, molecular subtypes, recurrence events and drug responses were validated across cohorts. In addition, the learned patch-level attention scores enabled us to generate heatmaps that were highly consistent with pathologist annotations and spatial transcriptomic data. These findings demonstrated that our model effectively established the high-order genotype-phenotype associations, thereby potentially extend the application of digital pathology in clinical practice.

Factors associated with the discrepancy between objective and subjective cognitive impairment in bipolar disorder

ObjectiveThe aim of this study is to evaluate the discrepancy between objective cognitive measures and cognitive subjective complaints in a sample of euthymic patients with bipolar disorder (BD). MethodsOne hundred and sixteen participants (83 euthymic patients with BD and 33 healthy controls) were enrolled for this study. Patients were assessed with a comprehensive neuropsychological battery and they also reported their subjective cognitive complaints with the Cognitive Complaints in Bipolar Disorder Rating Scale (COBRA). The discrepancy between objective and subjective data was calculated using a novel methodology proposed in a previous study (Miskowiak, 2016). Statistical analyses included Pearson correlations and multiple linear regression. ResultsHigher number of previous depressive episodes was identified as one variable associated with the global sensitivity composite score (Beta = 0.25; t = 2.1; p = 0.04) and with the verbal learning and memory sensitivity score (Beta = 0.26; t = 2.16; p = 0.03). That is, patients with more previous depressive episodes tend to over-report cognitive complaints. In contrast, higher number of previous hospitalizations was associated with stoicism in the global total score (Beta = −0.27; t = −2.24: p = 0.029) and in the domain of attention/processing speed (Beta = −0.34; t = −2.52; p = 0.016), indicating patients with more hospitalizations tend to report less cognitive complaints. DiscussionOur study identified some factors that might help to explain the discrepancy between objective and subjective cognitive measures in BD, including number of previous depressive episodes and number of previous hospitalizations. This highlights the need of the combined use of both types of cognitive measures to make an accurate assessment of cognitive dysfunctions and their effective treatment.

Evaluating the online Resilience Skills Enhancement programme among undergraduate students: A double-blind parallel randomized controlled trial.

Resilience training has beneficial effects on the ability of undergraduate students to withstand adversity and stress. However, there are inconsistencies in the content and delivery approaches for resilience training. Given the increasing shifts towards computer-assisted instruction, there is a need to develop and evaluate innovative approaches for resilience training. This study aimed to examine the efficacy of two versions of the Resilience Skills Enhancement (RISE) programme. A randomized controlled trial was used to evaluate the effects of blended learning (BL) and a self-guided RISE programme on the resilience, social support, and learning outcomes of undergraduate students. One hundred and fourteen students were recruited and randomly allocated to receive either BL or the self-guided RISE programme. The within-group analyses indicated significant improvements in resilience scores for the BL (F=37.74, p<0.001) and self-guided groups (F=10.16, p<0.001) with moderate (d=0.62, 95% CI: 0.25, 1.00) to large effect sizes (d=1.11, 95% CI: 0.71, 1.50) respectively. There were also significant differences across the three time points for social support scores for the BL (F=4.50, p<0.05) and self-guided groups (F=4.59, p<0.05). Students in the BL group had considerable improvements in self-efficacy of learning and performance (F=5.42, p<0.01) and meta-cognitive self-regulation scores (F=5.91, p<0.01). In the between-group analyses, both BL and self-guided RISE were comparable for resilience, social support, and learning scores (p>0.05). The study provided preliminary evidence that both modes, BL and self-guided RISE programme lead to positive effects on the resilience, social support, and learning scores of students.

A Model Study of Strategies for Optimizing the Educational Environment and Enhancing Student Engagement in Civics and Political Science Courses

Abstract This paper briefly outlines the direction of optimization of the civic education environment, proposes a civic education resource sharing model and an incentive model for educational resource sharing using the resource sharing approach, defines the main body of resource sharing, and establishes a model of the incentive system with students as the main body. Analyze the symbolic meaning of the model and propose the assumption of an educational resource incentive. Combine the revenue maximization function, participation constraints, and incentive constraints to form the incentive model. Using the decision tree algorithm of data mining technology for student participation behavior mining, we get the overall participation average score of undergraduate group cooperative learning, which is 3.576, the overall level of involvement is moderately high. The grade level trend of undergraduate group cooperative learning participation is freshman&gt;fourth year&gt;three years&gt;sophomore years. Combined with the simulation results of the incentive model to analyze the role of the incentive model on the behavioral input of student engagement, the standard error of regression of the teaching incentive of the educational environment of the Civics class on student engagement behavior is 0.473, and the overall mean of the teaching incentive perceived by the students is 3.77, which indicates that the incentive strategy implemented in the educational environment of the Civics class is more satisfactory to the students.

Enhancing Learner Engagement in Online Education through ADDG9E Model

Abstract—Nowadays, Online Education has been an inevitable choice of practice in the teaching-learning process. Learner engagement is one of the ineluctable challenges faced by the course teachers. For sustaining the learner's engagement throughout the course, teachers have to plan teaching-learning activities such as learning environment, content delivery, active student engagement, assessment, and evaluation. To plan teaching–learning activities effectively, the ADDG9E Instructional Design model, the combination of ADDIE and the Gagne’s 9 events models, has been proposed. Computer Programming CP101x – online bridge course has been offered to first-year engineering students. This course has been offered to improve the problem-solving skills of the learners. The proposed model has been implemented for organizing an online bridge course using the Moodle platform. The experimental results show that the proposed model has improved the learner's engagement, sustainability in learning, formative and summative assessment scores compared to physical classroom teaching. The course feedback shows that how well the learners have enjoyed their learning in the proposed ADDG9E Instructional Design modelbased online bridge course on computer programming. Keywords— ADDIE Model, Gagne’s 9 Events Model, Online Instructional Design, Online Learning, Online Course Design, Student Engagement, Self-Learning, Self-Motivation.

A Theoretical Study on College English Reading Teaching Based on SOLO Classification Theory

Abstract College English reading instruction represents a crucial component in enhancing students’ overall linguistic competencies. The advent of cognitive psychology has facilitated the application of the SOLO categorization theory as a novel approach to augmenting students’ proficiency in English reading. This study devises a teaching model tailored to college English reading based on the SOLO categorization theory and implements a systematic research design to evaluate its effectiveness. The efficacy of this model is assessed by comparing the changes in English reading learning attitudes, scores, and capabilities between an experimental group and a control group, using pre-and post-test results. The findings indicate that the mean difference in the English reading learning attitudes between the two groups expanded from 0.09 at the pre-test stage to 0.54 at the post-test stage. Moreover, the experimental group exhibited a substantially greater increase in English reading scores, with an improvement of 14.16, compared to 3.68 in the control group. In terms of English reading abilities, the experimental group outperformed the control group across four dimensions, with increases of 2.86, 1.04, 1.76, and 3.28, respectively. Overall, the university English reading teaching model grounded in SOLO classification theory significantly enhances students’ attitudes towards English reading and effectively boosts their reading achievements and capabilities.

Evaluating Feedback Comments in Entrustable Professional Activities: A Cross-Sectional Study.

Competency-based medical education (CBME) has transformed postgraduate medical training, prioritizing competency acquisition over traditional time-based curricula. Integral to CBME are Entrustable Professional Activities (EPAs), that aim to provide high-quality feedback for trainee development. Despite its importance, the quality of feedback within EPAs remains underexplored. We employed a cross-sectional study to explore feedback quality within EPAs, and to examine factors influencing length of written comments and their relationship to quality. We collected and analyzed 1163 written feedback comments using the Quality of Assessment for Learning (QuAL) score. The QuAL aims to evaluate written feedback from low-stakes workplace assessments, based on 3 quality criteria (evidence, suggestion, connection). Afterwards, we performed correlation and regression analyses to examine factors influencing feedback length and quality. EPAs facilitated high-quality written feedback, with a significant proportion of comments meeting quality criteria. Task-oriented and actionable feedback was prevalent, enhancing value of low-stakes workplace assessments. From the statistical analyses, the type of assessment tool significantly influenced feedback length and quality, implicating that direct and video observations can yield superior feedback in comparison to case-based discussions. However, no correlation between entrustment scores and feedback quality was found, suggesting potential discrepancies between the feedback and the score on the entrustability scale. This study indicates the role of the EPAs to foster high-quality feedback within CBME. It also highlights the multifaceted feedback dynamics, suggesting the influence of factors such as feedback length and assessment tool on feedback quality. Future research should further explore contextual factors for enhancing medical education practices.

A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis.

Introduction: Hepatic cystic echinococcosis (HCE) is a widely seen parasitic infection. Biological activity is crucial for treatment planning. This work aims to explore the potential applications of a deep learning radiomics (DLR) model, based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis. Methods: A retrospective analysis of 160 patients with hepatic echinococcosis was performed (127 and 33 in training and validation sets). Volume of interests (VOIs) were drawn, and radiomics features and deep neural network features were extracted. Feature selection was performed on the training set, and radiomics score (Rad Score) and deep learning score (Deep Score) were calculated. Seven diagnostics models (based on logistic regression algorithm) for the biological activity grading were constructed using the selected radiomics features and two deep model features respectively. All models were evaluated using the receiver operating characteristic curve, and the area under the curve (AUC) was calculated. A nomogram was constructed using the combined model, and its calibration, discriminatory ability, and clinical utility were assessed. Results: 12, 6 and 10 optimal radiomics features, deep learning features were selected from two deep learning network (DLN) features, respectively. For biological activity grading of hepatic cystic echinococcosis, the combined model demonstrated strong diagnostic performance, with an AUC value of 0.888 (95% CI: 0.837-0.936) in the training set and 0.876 (0.761-0.964) in the validation set. The clinical decision analysis curve indicated promising results, while the calibration curve revealed that the nomogram's prediction result was highly compatible with the actual result. Conclusion: The DLR model can be used for predicting the biological activity grading of hepatic echinococcosis.

The effect of escape room clinical evaluation method on satisfaction, learning, and preparedness to practice as interns of nursing students: A quasi-experimental quantitative study.

Clinical evaluation utilizing the escape room game is recognized as a novel method for assessing the team-oriented performance of learners. It is a tool for evaluating teamwork skills in clinical settings, which can boost student motivation and learning. This study aimed to investigate the effect of clinical evaluation through escape rooms and feedback provision through the PEARLS (promoting excellence and reflective learning in simulation) approach on pre-internship nursing students' satisfaction, learning, and preparedness to practice as interns. The current research is a quasi-experimental quantitative study conducted with a census sample of 42 sixth-semester undergraduate nursing students in 2022. The escape room method was utilized to evaluate entry preparedness into the clinical field. Reliable and valid researcher-made questionnaires were administered to assess the impact of the intervention on learning, satisfaction, and preparedness. The data were analyzed in SPSS version 26 using descriptive and inferential tests. The significance level was considered to be less than 0.05. Twenty-six males and 16 females constituted the 42 participants (mean age: 23.46 years). The clinical evaluation method of the escape room game was deemed satisfactory or highly satisfactory by 80% of students. From the perspective of 72% of students, escape rooms were definitely or highly effective in shaping their preparedness to enter the clinical field. Comparing the students' mean learning scores (self-assessment of clinical skills) before and after the test revealed that their post-test scores (55/16 ± 13/33) were significantly higher than their pre-test scores (45/58 ± 16/58) (P < 0.001). It appears that using the escape room evaluation method in conjunction with other student evaluation methods has helped improve students' interpersonal communication, problem-solving skills, critical thinking, and teamwork. The experience of working in a group not only improves these skills and is enjoyable to students but can also enhance their learning. Clinical education is dependent on teamwork. The escape room test can be viewed as a valuable tool for encouraging students to collaborate as a team. Therefore, it is suggested that students in all medical education groups be evaluated clinically using this test.